A Step-by-Step Guide for Data Scraping
The Machine Age of Customer Insight
ISBN: 978-1-83909-697-6, eISBN: 978-1-83909-694-5
Publication date: 15 March 2021
Abstract
Every second, vast amounts of data are generated and stored on the Internet. Data scraping makes these data accessible and usable for business and scientific purposes. Web-scraped data are of high value to businesses as they can be used to inform many strategic decisions such as pricing or market positioning. Although it is not difficult to scrape data, particularly when they come from public websites, there are six key steps that analysts should ideally consider and follow. Following these steps can help to better harness the business value of online data.
Keywords
Acknowledgements
Acknowledgments
Part of this content was taken from the data scraping courses Professor Hofstetter teaches at the University of Lucerne (“Data Analytics and Decision Support”) and the University of St. Gallen (“Data Scraping and Management for Social Scientists”). Scraping examples were partly taken from the working paper by Hofstetter, R., Nair, H., and Misra, S., Can Open Innovation Survive? Imitation and Return on Originality in Crowdsourcing Creative Work (January 14, 2020). Stanford University Graduate School of Business Research Paper No. 18-11. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3133158.
Citation
Hofstetter, R. (2021), "A Step-by-Step Guide for Data Scraping", Einhorn, M., Löffler, M., de Bellis, E., Herrmann, A. and Burghartz, P. (Ed.) The Machine Age of Customer Insight, Emerald Publishing Limited, Leeds, pp. 129-143. https://doi.org/10.1108/978-1-83909-694-520211013
Publisher
:Emerald Publishing Limited
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